The present application relates to the technical field of automatic
assembly, and particularly relates to a mechanical automatic
assembly system based on
artificial intelligence. The present application acquires
pose data and environment state of an
assembly executor in real time through a
perception positioning module, constructs an expected twin model according to the
pose data and the environment state, divides a running period into a plurality of sub-periods to evaluate joint trajectory dispersion, determines a set of monitoring time points for a low-dispersion sub-period and calculates instantaneous deviation based on the dispersion
classification result, selectively monitors abnormalities, calculates tracking errors for a high-dispersion sub-period in real time, generates an
error tolerance threshold based on illumination intensity and environment image interference, and finally determines abnormalities according to the
tracking error representation value and the
error tolerance threshold. When facing a large number of monitoring targets, the present application adopts different
error analysis strategies for different sub-periods, introduces a dynamic threshold adjustment mechanism, thereby saving monitoring computing power and improving monitoring accuracy and reliability.